Using ensemble classifier for small bowel ulcer detection in wireless capsule endoscopy images

Authors:
Baopu Li;Lin Qi;Max Q.-H. Meng;Yichen Fan
Affiliations:
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, SAR, China;Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, SAR, China;Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, SAR, China;Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, SAR, China
Venue:
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Year:
2009

Citing 9
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
ROC curves and video analysis optimization in intestinal capsule endoscopy

Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments

Computers in Biology and Medicine
Texture analysis for ulcer detection in capsule endoscopy images

Image and Vision Computing
Application of majority voting to pattern recognition: an analysis of its behavior and performance

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
MPEG-7 Visual Descriptors—Contributions for Automated Feature Extraction in Capsule Endoscopy

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Wireless capsule endoscopy (WCE) has been widely applied in hospitals due to its great advantage that it can directly view the entire small bowel in human body compared with traditional endoscopies and other imaging techniques for gastrointestinal diseases. However, the large number of the images it produced during each test is a great burden for physicians to inspect. To relief the clinicians it is of great importance to develop computer assisted diagnosis system. In this paper, a new computer aided detection scheme aimed for small bowel ulcer detection of WCE images is proposed. This new scheme utilizes an ensemble classifier, which is build upon K nearest neighborhood (KNN), multilayer perceptron (MLP) neural network and support vector machine (SVM), to detect small intestine ulcer WCE images. As far as we know, the combination of multiple classifiers in the field of endoscopic images has never been studied before. Experiments on our present image data show that it is promising to employ the proposed hybrid classifier to recognize the small bowel ulcer WCE images.